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Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration
Adam is one of the most influential adaptive stochastic algorithms for training deep neural networks, which has been pointed out to be divergent even in the simple convex setting via a few simple counterexamples.
A Sufficient Condition for Convergences of Adam and RMSProp
Adam and RMSProp are two of the most influential adaptive stochastic algorithms for training deep neural networks, which have been pointed out to be divergent even in the convex setting via a few simple counterexamples.
A Unified Analysis of AdaGrad with Weighted Aggregation and Momentum Acceleration
Integrating adaptive learning rate and momentum techniques into SGD leads to a large class of efficiently accelerated adaptive stochastic algorithms, such as AdaGrad, RMSProp, Adam, AccAdaGrad, \textit{etc}.
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